DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

F

Fuzzy Expert System: Uses Fuzzy Logic instead of Boolean Logic for reasoning in an Expert Systems.

Fuzzy Logic: Introduced by Dr. Lotfi Zadeh of UCB around 1960. A superset of conventional Boolean logic that allows truth values in the interval $[0,1]$, instead of just the set $\{0, 1\}$ with a rigorous set of mathematical operations that define how the logic system works. The most common definitions for the basic operations are:

$$truth(x AND y) = min (truth(x), truth(y)); truth(x OR y) = max (truth(x), truth(y)); truth(NOT x) = 1 - truth(x),$$

where x and y are truth values (degree of belief, degree of membership).

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